Objective To explore the risk factors for intracranial infection in patients after neurosurgery, construct and validate a Nomogram prediction model. Methods Data of 978 patients who underwent neurosurgery in a hospital in Nanjing from January 1, 2019 to December 31, 2022 were retrospectively analyzed. Independent risk factors were screened through logistic univariate and multivariate analyses. Modeling variables were screened through Lasso regression. A Nomogram model was constructed and internally validated by logistic regression. Effectiveness of the model was evaluated with receiver operating characteristic (ROC) curve, calibration curve and decision curve. Results Among 978 patients underwent neurosurgery, 293 had postoperative intracranial infection, with an incidence of healthcare-associated infection of 29.96%. There was no significant difference in age, gender, proportion of coronary heart disease, cerebral infarction, diabetes and hypertension between the infected group and the non-infected group (all P>0.05). Multivariate logistic analysis showed that postoperative intracranial hypertension, fever, increased neutrophil percentage in blood routine examination, turbid cerebrospinal fluid, positive Pan's test, decreased glucose concentration, abnormal ratio of cerebrospinal fluid/serum glucose, positive microbial culture, absence of indwelling external ventricular drainage tubes, presence of indwelling lumbar cistern drainage tubes, use of immunosuppressive agents, and long duration of surgery were independent risk factors for postoperative intracranial infection in patients who underwent neurosurgery (all P < 0.05). Fifteen variables were screened out through Lasso regression. Fourteen variables were finally included for modeling after collinear screening, missing data imputation (random forest method) and checking pairwise interaction items. A Nomogram prediction model was constructed, with the area under ROC curve, sensitivity, specificity, and accuracy of 0.885, 0.578, 0.896, and 0.704, respectively. Internal validation of the model was conducted. The modeling and validation groups presented similar effects. The calibration curve and decision curve also indicated that the model had good predictive efficacy. Conclusion The constructed Nomogram prediction model for postoperative intracranial infection after neurosurgery is scientific, and the prediction indicators are easy to obtain. The model presents with high stability, reliability, and application value, thus can provide reference for the assessment of postoperative intracranial infection after neurosurgery.